{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- This should be added to the overrides/main.html and improved-->\n",
    "<div class=\"grid cards\" markdown>\n",
    "\n",
    "- <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"50\" width=\"50\" viewBox=\"0 0 488 512\"><!--!Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.--><path fill=\"#2094F3\" d=\"M488 261.8C488 403.3 391.1 504 248 504 110.8 504 0 393.2 0 256S110.8 8 248 8c66.8 0 123 24.5 166.3 64.9l-67.5 64.9C258.5 52.6 94.3 116.6 94.3 256c0 86.5 69.1 156.6 153.7 156.6 98.2 0 135-70.4 140.8-106.9H248v-85.3h236.1c2.3 12.7 3.9 24.9 3.9 41.4z\"/></svg>\n",
    "<a href=\"https://colab.research.google.com/github/AmbiqAI/heartkit/blob/main/docs/guides/byot.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
    "    View in Colab\n",
    "</a>\n",
    "\n",
    "- <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"50\" width=\"50\" viewBox=\"0 0 496 512\"><!--!Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free Copyright 2024 Fonticons, Inc.--><path fill=\"#2094F3\" d=\"M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3 .3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5 .3-6.2 2.3zm44.2-1.7c-2.9 .7-4.9 2.6-4.6 4.9 .3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3 .7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3 .3 2.9 2.3 3.9 1.6 1 3.6 .7 4.3-.7 .7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3 .7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3 .7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z\"/></svg>\n",
    "<a href=\"https://github.com/AmbiqAI/heartkit/blob/main/docs/guides/byot.ipynb\" class=\"md-content__button md-icon\" style=\"color: #2094F3;\">\n",
    "    GitHub source\n",
    "</a>\n",
    "\n",
    "</div>\n",
    "\n",
    "# Bring-Your-Own-Task (BYOT)\n",
    "\n",
    "__Date created:__ 2024/08/15 \n",
    "\n",
    "__Last Modified:__ 2024/08/15 \n",
    "\n",
    "__Description:__ Create custom task for HeartKit end-to-end\n",
    "\n",
    "## Overview \n",
    "\n",
    "In this notebook, we provide a complete walkthrough of creating a custom task. To keep things simple, we will create a task that will predict heart rate from raw ECG signal.\n",
    "\n",
    "Below we outline the high-level steps to create a custom task:\n",
    "\n",
    "1. Identify datasets and create corresponding dataloaders (e.g. PTB-XL)\n",
    "2. Create data pipeline for training, validation, and test sets\n",
    "3. Implement task routines for modes: __train__, __evaluate__, __export__ and optionally __demo__. \n",
    "\n",
    "In this example, we will implement only __train__ and __evaluate__ modes.\n",
    "\n",
    "__Datasets__\n",
    "\n",
    "- **[PTB-XL](https://ambiqai.github.io/heartkit/datasets/ptbxl/)**: The PTB-XL is a large publicly available electrocardiography dataset. \n",
    "It contains 21837 clinical 12-lead ECGs from 18885 patients of 10 second length. The ECGs are sampled at 500 Hz and are annotated by up to two cardiologists.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-16 15:31:46.467589: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-08-16 15:31:46.475433: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-08-16 15:31:46.477772: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
    "import random\n",
    "from typing import Generator\n",
    "from collections.abc import Iterable\n",
    "from pathlib import Path\n",
    "import tempfile\n",
    "\n",
    "import keras\n",
    "import heartkit as hk\n",
    "import physiokit as pk\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import numpy.typing as npt\n",
    "import neuralspot_edge as nse\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Be sure to set the dataset path to the correct location\n",
    "os.environ['HK_DATASET_PATH'] = os.getenv('HK_DATASET_PATH', './datasets')\n",
    "\n",
    "plot_theme = hk.utils.dark_theme\n",
    "nse.utils.silence_tensorflow()\n",
    "_ = hk.utils.setup_plotting(plot_theme)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Create Dataloaders\n",
    "\n",
    "We will create a dataloader class for the dataset __PTB-XL__ since it provides heart beat locations via `blabels`. \n",
    "\n",
    "Given a raw ECG signal, we will compute the heart rate given the beat locations in the frame. The rate will be calculated based on the RR intervals using PhysioKit. The output will be the ecg signal and the heart rate in beats per second.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PtbxlDataloader(hk.HKDataloader):\n",
    "    def __init__(self, ds: hk.datasets.PtbxlDataset, **kwargs):\n",
    "        \"\"\"Dataloader for PTB-XL to generate HeartRateTask data.\"\"\"\n",
    "        super().__init__(ds=ds, **kwargs)\n",
    "\n",
    "    def patient_data_generator(\n",
    "        self,\n",
    "        patient_id: int,\n",
    "        samples_per_patient: int,\n",
    "    ):\n",
    "        # Compute input size (might be different due to sampling rate)\n",
    "        input_size = int(np.ceil((self.ds.sampling_rate / self.sampling_rate) * self.frame_size))\n",
    "\n",
    "        with self.ds.patient_data(patient_id) as h5:\n",
    "            ecg = h5[\"data\"][:]\n",
    "            # Beat locations. Convert 100Hz to ds.sampling_rate\n",
    "            blabels = h5[\"blabels\"][:, 0]*(self.ds.sampling_rate/100.0)\n",
    "        # END WITH\n",
    "\n",
    "        for _ in range(samples_per_patient):\n",
    "            # Select random lead and frame location\n",
    "            lead = random.choice(self.ds.leads)\n",
    "            frame_start = np.random.randint(0, ecg.shape[1] - input_size)\n",
    "            frame_end = frame_start + input_size\n",
    "\n",
    "            # Compute BPM by selecting beats within frame, computing RR intervals and averaging\n",
    "            frame_blabels = blabels[(blabels >= frame_start) & (blabels < frame_end)]\n",
    "            rri = pk.ecg.compute_rr_intervals(frame_blabels)\n",
    "            bpm = 60.0 / (np.nanmean(rri) / self.ds.sampling_rate)\n",
    "\n",
    "            # Extract ecg frame\n",
    "            x = ecg[lead, frame_start:frame_end].copy()\n",
    "\n",
    "            # Resample if needed\n",
    "            if self.ds.sampling_rate != self.sampling_rate:\n",
    "                x = pk.signal.resample_signal(x, self.ds.sampling_rate, self.sampling_rate, axis=0)\n",
    "                x = x[:self.frame_size]  # Ensure frame size\n",
    "\n",
    "            x = np.nan_to_num(x).astype(np.float32)\n",
    "            x = x.reshape(-1, 1)\n",
    "            y = bpm / 60.0 # Make beats per second\n",
    "            yield x, y\n",
    "        # END FOR\n",
    "\n",
    "    def data_generator(\n",
    "        self,\n",
    "        patient_ids: list[int],\n",
    "        samples_per_patient: int | list[int],\n",
    "        shuffle: bool = False,\n",
    "    ) -> Generator[tuple[npt.NDArray, npt.NDArray], None, None]:\n",
    "        if isinstance(samples_per_patient, Iterable):\n",
    "            samples_per_patient = samples_per_patient[0]\n",
    "        for pt_id in nse.utils.uniform_id_generator(patient_ids, shuffle=shuffle):\n",
    "            for x, y in self.patient_data_generator(pt_id, samples_per_patient):\n",
    "                yield x, y\n",
    "            # END FOR\n",
    "        # END FOR\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize output of dataloader\n",
    "\n",
    "We will grab a single sample from the dataloader and visualize the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = hk.DatasetFactory.get(\"ptbxl\")(\n",
    "    path=Path(os.environ['HK_DATASET_PATH']) / \"ptbxl\"\n",
    ")\n",
    "dl = PtbxlDataloader(\n",
    "    ds=ds,\n",
    "    frame_size=4000,\n",
    "    sampling_rate=500,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "patient_ids = np.random.permutation(ds.patient_ids)\n",
    "x, y = next(dl.data_generator(patient_ids=patient_ids, samples_per_patient=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(9, 4))\n",
    "ax.plot(x, label=f\"HR: {60*y:0.0f} BPM\")\n",
    "ax.set_title(\"ECG Frame\")\n",
    "ax.legend()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Register dataloaders to factory\n",
    "\n",
    "We will then create a simple DataloaderFactory to ease the creation of dataloaders based on dataset names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "DataloaderFactory = nse.utils.create_factory(factory=\"BYOT.DataloaderFactory\", type=hk.HKDataloader)\n",
    "DataloaderFactory.register(\"ptbxl\", PtbxlDataloader)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Create Data Pipeline\n",
    "\n",
    "We will create a data pipeline that will be used to train and evaluate the model. For each dataset, we will:\n",
    "1. Load the dataset via `hk.DatasetFactory`\n",
    "1. Split the dataset patients into training and validation sets\n",
    "1. Load the corresponding dataloader via `hk.DataloaderFactory` and create a `tf.data.Dataset` for training and validation\n",
    "\n",
    "Once each dataset has a pair of training and validation datasets, we will combine them into a single training and validation dataset. At this point we will then extend the pipeline by adding the following:\n",
    "1. Shuffle the dataset (if training)\n",
    "1. Batch the dataset\n",
    "1. Apply augmentations/preprocessing (if any)\n",
    "1. Prefetch the dataset\n",
    "\n",
    "Lastly, for the validation set will cache which will (1) speed up the evaluation process and (2) ensure that the same validation set is used for each epoch with fixed size.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_data_pipeline(\n",
    "    ds: tf.data.Dataset,\n",
    "    sampling_rate: int,\n",
    "    batch_size: int,\n",
    "    buffer_size: int | None = None,\n",
    "    augmentations: list[hk.NamedParams] | None = None,\n",
    ") -> tf.data.Dataset:\n",
    "    \"\"\"Transforms a dataset into a pipeline with augmentations.\n",
    "\n",
    "    Args:\n",
    "        ds(tf.data.Dataset): Input dataset.\n",
    "        sampling_rate(int): Sampling rate of the dataset.\n",
    "        batch_size(int): Batch size.\n",
    "        buffer_size(int | None): Buffer size for shuffling.\n",
    "        augmentations(list[hk.NamedParams] | None): List of augmentations to apply.\n",
    "\n",
    "    Returns:\n",
    "        tf.data.Dataset: Augmented dataset\n",
    "    \"\"\"\n",
    "    if buffer_size:\n",
    "        ds = ds.shuffle(\n",
    "            buffer_size=buffer_size,\n",
    "            reshuffle_each_iteration=True,\n",
    "        )\n",
    "    if batch_size:\n",
    "        ds = ds.batch(\n",
    "            batch_size=batch_size,\n",
    "            drop_remainder=True,\n",
    "            num_parallel_calls=tf.data.AUTOTUNE,\n",
    "        )\n",
    "    augmenter = hk.datasets.create_augmentation_pipeline(\n",
    "        augmentations,\n",
    "        sampling_rate=sampling_rate\n",
    "    )\n",
    "\n",
    "    ds = (\n",
    "        ds.map(\n",
    "            lambda data, labels: {\n",
    "                \"data\": tf.cast(data, \"float32\"),\n",
    "                \"labels\": tf.cast(labels, \"float32\"),\n",
    "            },\n",
    "            num_parallel_calls=tf.data.AUTOTUNE,\n",
    "        )\n",
    "        .map(\n",
    "            augmenter,\n",
    "            num_parallel_calls=tf.data.AUTOTUNE,\n",
    "        )\n",
    "        .map(\n",
    "            lambda data: (data[\"data\"], data[\"labels\"]),\n",
    "            num_parallel_calls=tf.data.AUTOTUNE,\n",
    "        )\n",
    "    )\n",
    "    return ds.prefetch(tf.data.AUTOTUNE)\n",
    "\n",
    "def load_train_datasets(\n",
    "    datasets: list[hk.HKDataset],\n",
    "    dataloaderFactory: nse.utils.ItemFactory[hk.HKDataloader],\n",
    "    params: hk.HKTaskParams,\n",
    ") -> tuple[tf.data.Dataset, tf.data.Dataset]:\n",
    "    \"\"\"Loads training and validation datasets.\n",
    "\n",
    "    Args:\n",
    "        datasets(list[hk.HKDataset]): List of datasets to load.\n",
    "        dataloaderFactory(nse.utils.ItemFactory[hk.HKDataloader]): Factory to create dataloaders.\n",
    "        params(hk.HKTaskParams): Task parameters.\n",
    "\n",
    "    Returns:\n",
    "        tuple[tf.data.Dataset, tf.data.Dataset]: Training and validation datasets.\n",
    "    \"\"\"\n",
    "\n",
    "    # This will load each dataset/dataloader, split subjects, and merge into single tf.data.Dataset\n",
    "    train_ds, val_ds = hk.tasks.utils.load_train_dataloader_split(datasets, params, factory=DataloaderFactory)\n",
    "\n",
    "    # Create training and validation pipelines\n",
    "    train_ds = create_data_pipeline(\n",
    "        ds=train_ds,\n",
    "        sampling_rate=params.sampling_rate,\n",
    "        batch_size=params.batch_size,\n",
    "        buffer_size=params.buffer_size,\n",
    "        augmentations=params.augmentations + params.preprocesses,\n",
    "    )\n",
    "\n",
    "    val_ds = create_data_pipeline(\n",
    "        ds=val_ds,\n",
    "        sampling_rate=params.sampling_rate,\n",
    "        batch_size=params.batch_size,\n",
    "        augmentations=params.preprocesses,\n",
    "    )\n",
    "\n",
    "    # Cache validation dataset\n",
    "    val_steps_per_epoch = params.val_size // params.batch_size if params.val_size else params.val_steps_per_epoch\n",
    "    val_steps_per_epoch = val_steps_per_epoch or 50\n",
    "    val_ds = val_ds.take(val_steps_per_epoch).cache()\n",
    "\n",
    "    return train_ds, val_ds\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Create task routines\n",
    "\n",
    "We will create a task that will predict heart rate from raw ECG signal. The task will have the following modes:\n",
    "1. __train__: Train the model\n",
    "1. __evaluate__: Evaluate the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(params: hk.HKTaskParams):\n",
    "    \"\"\"Train  model\n",
    "\n",
    "    Args:\n",
    "        params (hk.HKTaskParams): Training parameters\n",
    "    \"\"\"\n",
    "    os.makedirs(params.job_dir, exist_ok=True)\n",
    "\n",
    "    logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"train.log\")\n",
    "    logger.debug(f\"Creating working directory in {params.job_dir}\")\n",
    "\n",
    "    params.seed = nse.utils.set_random_seed(params.seed)\n",
    "    logger.debug(f\"Random seed {params.seed}\")\n",
    "\n",
    "    with open(params.job_dir / \"train_config.json\", \"w\", encoding=\"utf-8\") as fp:\n",
    "        fp.write(params.model_dump_json(indent=2))\n",
    "\n",
    "    params.num_classes = 1 # Regression\n",
    "    feat_shape = (params.frame_size, 1)\n",
    "\n",
    "    datasets = [hk.DatasetFactory.get(ds.name)(**ds.params) for ds in params.datasets]\n",
    "\n",
    "    train_ds, val_ds = load_train_datasets(\n",
    "        datasets=datasets,\n",
    "        dataloaderFactory=DataloaderFactory,\n",
    "        params=params\n",
    "    )\n",
    "\n",
    "    y_true = np.concatenate([y for _, y in val_ds.as_numpy_iterator()])\n",
    "    y_true = np.argmax(y_true, axis=-1).flatten()\n",
    "\n",
    "    inputs = keras.Input(shape=feat_shape, name=\"input\", dtype=\"float32\")\n",
    "\n",
    "    # Load existing model\n",
    "    if params.resume and params.model_file:\n",
    "        logger.debug(f\"Loading model from file {params.model_file}\")\n",
    "        model = nse.models.load_model(params.model_file)\n",
    "        params.model_file = None\n",
    "    else:\n",
    "        logger.debug(\"Creating model from scratch\")\n",
    "        if params.architecture is None:\n",
    "            raise ValueError(\"Model architecture must be specified\")\n",
    "        model = hk.ModelFactory.get(params.architecture.name)(\n",
    "            inputs=inputs,\n",
    "            params=params.architecture.params,\n",
    "            num_classes=params.num_classes,\n",
    "        )\n",
    "    # END IF\n",
    "\n",
    "    flops = nse.metrics.flops.get_flops(model, batch_size=1, fpath=params.job_dir / \"model_flops.log\")\n",
    "\n",
    "    t_mul = 1\n",
    "    first_steps = (params.steps_per_epoch * params.epochs) / (np.power(params.lr_cycles, t_mul) - t_mul + 1)\n",
    "    scheduler = keras.optimizers.schedules.CosineDecayRestarts(\n",
    "        initial_learning_rate=params.lr_rate,\n",
    "        first_decay_steps=np.ceil(first_steps),\n",
    "        t_mul=t_mul,\n",
    "        m_mul=0.5,\n",
    "    )\n",
    "\n",
    "    optimizer = keras.optimizers.Adam(scheduler)\n",
    "    loss = keras.losses.MeanSquaredError()\n",
    "    metrics = [\n",
    "        keras.metrics.MeanAbsoluteError(name=\"mae\"),\n",
    "        keras.metrics.MeanSquaredError(name=\"mse\"),\n",
    "        keras.metrics.R2Score(name=\"rsq\"),\n",
    "    ]\n",
    "\n",
    "    if params.model_file is None:\n",
    "        params.model_file = params.job_dir / \"model.keras\"\n",
    "\n",
    "    model.compile(optimizer=optimizer, loss=loss, metrics=metrics)\n",
    "    logger.debug(f\"Model requires {flops/1e6:0.2f} MFLOPS\")\n",
    "\n",
    "    model_callbacks = [\n",
    "        keras.callbacks.EarlyStopping(\n",
    "            monitor=f\"val_{params.val_metric}\",\n",
    "            patience=max(int(0.25 * params.epochs), 1),\n",
    "            mode=\"max\" if params.val_metric == \"f1\" else \"auto\",\n",
    "            restore_best_weights=True,\n",
    "            verbose=min(params.verbose - 1, 1),\n",
    "        ),\n",
    "        keras.callbacks.ModelCheckpoint(\n",
    "            filepath=str(params.model_file),\n",
    "            monitor=f\"val_{params.val_metric}\",\n",
    "            save_best_only=True,\n",
    "            mode=\"max\" if params.val_metric == \"f1\" else \"auto\",\n",
    "            verbose=min(params.verbose - 1, 1),\n",
    "        ),\n",
    "        keras.callbacks.CSVLogger(params.job_dir / \"history.csv\"),\n",
    "    ]\n",
    "\n",
    "    history = model.fit(\n",
    "        train_ds,\n",
    "        steps_per_epoch=params.steps_per_epoch,\n",
    "        verbose=params.verbose,\n",
    "        epochs=params.epochs,\n",
    "        validation_data=val_ds,\n",
    "        callbacks=model_callbacks,\n",
    "    )\n",
    "    logger.debug(f\"Model saved to {params.model_file}\")\n",
    "\n",
    "    nse.plotting.plot_history_metrics(\n",
    "        history.history,\n",
    "        metrics=[\"loss\", metrics[0].name],\n",
    "        save_path=params.job_dir / \"history.png\",\n",
    "        title=\"Training History\",\n",
    "        stack=True,\n",
    "        figsize=(9, 5),\n",
    "    )\n",
    "\n",
    "    # Summarize results\n",
    "    rst = model.evaluate(val_ds, return_dict=True)\n",
    "    logger.info(f\"[VAL SET] \" + \", \".join(f\"{k.upper()}={v:.4f}\" for k, v in rst.items()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(params:hk.HKTaskParams):\n",
    "    \"\"\"Evaluate model\n",
    "\n",
    "    Args:\n",
    "        params (HKTaskParams): Evaluation parameters\n",
    "    \"\"\"\n",
    "    os.makedirs(params.job_dir, exist_ok=True)\n",
    "    logger = nse.utils.setup_logger(__name__, level=params.verbose, file_path=params.job_dir / \"test.log\")\n",
    "    logger.debug(f\"Creating working directory in {params.job_dir}\")\n",
    "\n",
    "    params.seed = nse.utils.set_random_seed(params.seed)\n",
    "    logger.debug(f\"Random seed {params.seed}\")\n",
    "\n",
    "    datasets = [hk.DatasetFactory.get(ds.name)(**ds.params) for ds in params.datasets]\n",
    "\n",
    "    _, test_ds = load_train_datasets(\n",
    "        datasets=datasets,\n",
    "        dataloaderFactory=DataloaderFactory,\n",
    "        params=params\n",
    "    )\n",
    "    test_x = np.concatenate([x for x, _ in test_ds.as_numpy_iterator()])\n",
    "    test_y = np.concatenate([y for _, y in test_ds.as_numpy_iterator()])\n",
    "\n",
    "    logger.debug(\"Loading model\")\n",
    "    model = nse.models.load_model(params.model_file)\n",
    "\n",
    "    logger.debug(\"Performing inference\")\n",
    "    rst = model.evaluate(test_ds, verbose=params.verbose, return_dict=True)\n",
    "    logger.info(\"[TEST SET] \" + \", \".join([f\"{k.upper()}={v:.2%}\" for k, v in rst.items()]))\n",
    "\n",
    "    y_pred = model.predict(test_x)\n",
    "\n",
    "    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(9, 4))\n",
    "    ax.scatter(y_pred*60, test_y*60)\n",
    "    ax.set_title(\"Predicted vs True BPM\")\n",
    "    ax.set_xlabel(\"Predicted BPM\")\n",
    "    ax.set_ylabel(\"True BPM\")\n",
    "    ax.annotate(f\"R2={rst['rsq']:.2f}\", xy=(0.05, 0.95), xycoords='axes fraction')\n",
    "\n",
    "    fig.tight_layout()\n",
    "    fig.show()\n",
    "    fig.savefig(params.job_dir / \"bpm_plot.png\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create HeartRateTask class and register it to the factory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class HeartRateTask(hk.HKTask):\n",
    "    @staticmethod\n",
    "    def train(params: hk.HKTaskParams):\n",
    "        train(params)\n",
    "\n",
    "    @staticmethod\n",
    "    def evaluate(params: hk.HKTaskParams):\n",
    "        evaluate(params)\n",
    "\n",
    "    @staticmethod\n",
    "    def export(params: hk.HKTaskParams) -> None:\n",
    "        raise NotImplementedError(\"Export not implemented\")\n",
    "\n",
    "    @staticmethod\n",
    "    def demo(params: hk.HKTaskParams) -> None:\n",
    "        raise NotImplementedError(\"Demo not implemented\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "hk.TaskFactory.register(\"heartrate\", HeartRateTask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rhythm\n",
      "beat\n",
      "segmentation\n",
      "diagnostic\n",
      "denoise\n",
      "foundation\n",
      "translate\n",
      "heartrate\n"
     ]
    }
   ],
   "source": [
    "for task_name in hk.TaskFactory.list():\n",
    "    print(task_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Let's test out the new task!\n",
    "\n",
    "First we will create a task configuration with the following features:\n",
    "* Frame Size: 8 seconds\n",
    "* Dataset: PTB-XL\n",
    "* Model: `EfficientNetV2` with 4 MBConv blocks each depth 1\n",
    "* Batch Size: 256\n",
    "* Buffer Size: 20,000\n",
    "* Learning Rate: 1e-3\n",
    "* Preprocess: Z-score normalization\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = hk.HKTaskParams(\n",
    "    name=\"BYOT-HR\",\n",
    "    job_dir=Path(tempfile.gettempdir()) / \"hk-byot-hr\",\n",
    "    verbose=1,\n",
    "    datasets=[\n",
    "        hk.NamedParams(\n",
    "            name=\"ptbxl\",\n",
    "            params=dict(\n",
    "                path=Path(os.environ[\"HK_DATASET_PATH\"]) / \"ptbxl\",\n",
    "            ),\n",
    "        ),\n",
    "    ],\n",
    "    frame_size=4000,  # 8 seconds\n",
    "    sampling_rate=500,  # 500Hz\n",
    "    samples_per_patient=5,\n",
    "    val_samples_per_patient=5,\n",
    "    val_patients=0.2,\n",
    "    val_size=10000,\n",
    "    batch_size=256,\n",
    "    buffer_size=20000,\n",
    "    epochs=100,\n",
    "    steps_per_epoch=50,\n",
    "    lr_rate=1e-3,\n",
    "    lr_cycles=1,\n",
    "    val_metric=\"loss\",\n",
    "    preprocesses=[\n",
    "        hk.NamedParams(\n",
    "            name=\"layer_norm\",\n",
    "            params=dict(\n",
    "                epsilon=0.01,\n",
    "                name=\"znorm\"\n",
    "            ),\n",
    "        ),\n",
    "    ],\n",
    "    augmentations=[],\n",
    "    architecture=hk.NamedParams(\n",
    "        name=\"efficientnetv2\",\n",
    "        params=dict(\n",
    "            input_filters=8,\n",
    "            input_kernel_size=[1, 9],\n",
    "            input_strides=[1, 2],\n",
    "            blocks=[\n",
    "                {\"filters\": 16, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1,  \"se_ratio\": 2},\n",
    "                {\"filters\": 24, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1,  \"se_ratio\": 2},\n",
    "                {\"filters\": 32, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1,  \"se_ratio\": 2},\n",
    "                {\"filters\": 40, \"depth\": 1, \"kernel_size\": [1, 9], \"strides\": [1, 2], \"ex_ratio\": 1,  \"se_ratio\": 2}\n",
    "            ],\n",
    "            output_filters=0,\n",
    "            include_top=True,\n",
    "            use_logits=True\n",
    "        ),\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "task = hk.TaskFactory.get(\"heartrate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1723822309.458226  626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723822309.478233  626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723822309.478319  626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723822309.479299  626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723822309.479375  626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723822309.479420  626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723822309.529037  626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723822309.529123  626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1723822309.529179  626139 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1723822323.031206  626304 service.cc:146] XLA service 0x7471ec02c0b0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1723822323.031237  626304 service.cc:154]   StreamExecutor device (0): NVIDIA GeForce RTX 4090, Compute Capability 8.9\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m 5/50\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 1.8535 - mae: 1.2239 - mse: 1.8535 - rsq: -20.3693"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "I0000 00:00:1723822328.955479  626304 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 55ms/step - loss: 1.4214 - mae: 1.1194 - mse: 1.4214 - rsq: -16.4093 - val_loss: 0.7500 - val_mae: 0.8469 - val_mse: 0.7500 - val_rsq: -8.4336\n",
      "Epoch 2/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - loss: 0.4457 - mae: 0.6133 - mse: 0.4457 - rsq: -4.2974 - val_loss: 0.0573 - val_mae: 0.1936 - val_mse: 0.0573 - val_rsq: 0.2792\n",
      "Epoch 3/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 87ms/step - loss: 0.0722 - mae: 0.2064 - mse: 0.0722 - rsq: 0.0739 - val_loss: 0.0206 - val_mae: 0.0998 - val_mse: 0.0206 - val_rsq: 0.7403\n",
      "Epoch 4/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 92ms/step - loss: 0.0411 - mae: 0.1526 - mse: 0.0411 - rsq: 0.4376 - val_loss: 0.0178 - val_mae: 0.0924 - val_mse: 0.0178 - val_rsq: 0.7758\n",
      "Epoch 5/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 46ms/step - loss: 0.0388 - mae: 0.1436 - mse: 0.0388 - rsq: 0.5175 - val_loss: 0.0161 - val_mae: 0.0889 - val_mse: 0.0161 - val_rsq: 0.7972\n",
      "Epoch 6/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0316 - mae: 0.1323 - mse: 0.0316 - rsq: 0.5965 - val_loss: 0.0173 - val_mae: 0.0953 - val_mse: 0.0173 - val_rsq: 0.7820\n",
      "Epoch 7/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0287 - mae: 0.1265 - mse: 0.0287 - rsq: 0.6291 - val_loss: 0.0152 - val_mae: 0.0857 - val_mse: 0.0152 - val_rsq: 0.8089\n",
      "Epoch 8/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0266 - mae: 0.1204 - mse: 0.0266 - rsq: 0.6571 - val_loss: 0.0115 - val_mae: 0.0709 - val_mse: 0.0115 - val_rsq: 0.8550\n",
      "Epoch 9/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0251 - mae: 0.1161 - mse: 0.0251 - rsq: 0.6806 - val_loss: 0.0116 - val_mae: 0.0734 - val_mse: 0.0116 - val_rsq: 0.8537\n",
      "Epoch 10/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0225 - mae: 0.1125 - mse: 0.0225 - rsq: 0.7156 - val_loss: 0.0104 - val_mae: 0.0672 - val_mse: 0.0104 - val_rsq: 0.8689\n",
      "Epoch 11/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0235 - mae: 0.1115 - mse: 0.0235 - rsq: 0.7163 - val_loss: 0.0094 - val_mae: 0.0626 - val_mse: 0.0094 - val_rsq: 0.8821\n",
      "Epoch 12/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0206 - mae: 0.1054 - mse: 0.0206 - rsq: 0.7445 - val_loss: 0.0088 - val_mae: 0.0573 - val_mse: 0.0088 - val_rsq: 0.8890\n",
      "Epoch 13/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0209 - mae: 0.1055 - mse: 0.0209 - rsq: 0.7386 - val_loss: 0.0083 - val_mae: 0.0559 - val_mse: 0.0083 - val_rsq: 0.8951\n",
      "Epoch 14/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0194 - mae: 0.1026 - mse: 0.0194 - rsq: 0.7589 - val_loss: 0.0095 - val_mae: 0.0653 - val_mse: 0.0095 - val_rsq: 0.8807\n",
      "Epoch 15/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0201 - mae: 0.1032 - mse: 0.0201 - rsq: 0.7443 - val_loss: 0.0079 - val_mae: 0.0561 - val_mse: 0.0079 - val_rsq: 0.9010\n",
      "Epoch 16/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0179 - mae: 0.0993 - mse: 0.0179 - rsq: 0.7775 - val_loss: 0.0071 - val_mae: 0.0524 - val_mse: 0.0071 - val_rsq: 0.9104\n",
      "Epoch 17/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0176 - mae: 0.0985 - mse: 0.0176 - rsq: 0.7787 - val_loss: 0.0070 - val_mae: 0.0508 - val_mse: 0.0070 - val_rsq: 0.9126\n",
      "Epoch 18/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0169 - mae: 0.0968 - mse: 0.0169 - rsq: 0.7807 - val_loss: 0.0092 - val_mae: 0.0673 - val_mse: 0.0092 - val_rsq: 0.8847\n",
      "Epoch 19/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0167 - mae: 0.0959 - mse: 0.0167 - rsq: 0.7843 - val_loss: 0.0067 - val_mae: 0.0495 - val_mse: 0.0067 - val_rsq: 0.9155\n",
      "Epoch 20/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0169 - mae: 0.0953 - mse: 0.0169 - rsq: 0.7864 - val_loss: 0.0066 - val_mae: 0.0505 - val_mse: 0.0066 - val_rsq: 0.9164\n",
      "Epoch 21/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0164 - mae: 0.0944 - mse: 0.0164 - rsq: 0.7958 - val_loss: 0.0065 - val_mae: 0.0487 - val_mse: 0.0065 - val_rsq: 0.9185\n",
      "Epoch 22/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0173 - mae: 0.0963 - mse: 0.0173 - rsq: 0.7887 - val_loss: 0.0064 - val_mae: 0.0497 - val_mse: 0.0064 - val_rsq: 0.9197\n",
      "Epoch 23/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0158 - mae: 0.0935 - mse: 0.0158 - rsq: 0.7930 - val_loss: 0.0063 - val_mae: 0.0507 - val_mse: 0.0063 - val_rsq: 0.9203\n",
      "Epoch 24/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0150 - mae: 0.0926 - mse: 0.0150 - rsq: 0.8087 - val_loss: 0.0056 - val_mae: 0.0440 - val_mse: 0.0056 - val_rsq: 0.9297\n",
      "Epoch 25/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0151 - mae: 0.0919 - mse: 0.0151 - rsq: 0.8020 - val_loss: 0.0057 - val_mae: 0.0452 - val_mse: 0.0057 - val_rsq: 0.9282\n",
      "Epoch 26/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0153 - mae: 0.0909 - mse: 0.0153 - rsq: 0.8012 - val_loss: 0.0054 - val_mae: 0.0424 - val_mse: 0.0054 - val_rsq: 0.9324\n",
      "Epoch 27/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0171 - mae: 0.0913 - mse: 0.0171 - rsq: 0.7825 - val_loss: 0.0056 - val_mae: 0.0461 - val_mse: 0.0056 - val_rsq: 0.9291\n",
      "Epoch 28/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0148 - mae: 0.0891 - mse: 0.0148 - rsq: 0.8121 - val_loss: 0.0084 - val_mae: 0.0690 - val_mse: 0.0084 - val_rsq: 0.8945\n",
      "Epoch 29/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0152 - mae: 0.0902 - mse: 0.0152 - rsq: 0.8177 - val_loss: 0.0057 - val_mae: 0.0487 - val_mse: 0.0057 - val_rsq: 0.9284\n",
      "Epoch 30/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0142 - mae: 0.0892 - mse: 0.0142 - rsq: 0.8188 - val_loss: 0.0054 - val_mae: 0.0467 - val_mse: 0.0054 - val_rsq: 0.9321\n",
      "Epoch 31/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0139 - mae: 0.0886 - mse: 0.0139 - rsq: 0.8321 - val_loss: 0.0047 - val_mae: 0.0407 - val_mse: 0.0047 - val_rsq: 0.9409\n",
      "Epoch 32/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0133 - mae: 0.0868 - mse: 0.0133 - rsq: 0.8310 - val_loss: 0.0045 - val_mae: 0.0397 - val_mse: 0.0045 - val_rsq: 0.9435\n",
      "Epoch 33/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0138 - mae: 0.0869 - mse: 0.0138 - rsq: 0.8214 - val_loss: 0.0052 - val_mae: 0.0449 - val_mse: 0.0052 - val_rsq: 0.9352\n",
      "Epoch 34/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0140 - mae: 0.0856 - mse: 0.0140 - rsq: 0.8197 - val_loss: 0.0047 - val_mae: 0.0414 - val_mse: 0.0047 - val_rsq: 0.9404\n",
      "Epoch 35/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0150 - mae: 0.0859 - mse: 0.0150 - rsq: 0.7989 - val_loss: 0.0046 - val_mae: 0.0390 - val_mse: 0.0046 - val_rsq: 0.9425\n",
      "Epoch 36/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0136 - mae: 0.0861 - mse: 0.0136 - rsq: 0.8291 - val_loss: 0.0052 - val_mae: 0.0453 - val_mse: 0.0052 - val_rsq: 0.9344\n",
      "Epoch 37/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0141 - mae: 0.0872 - mse: 0.0141 - rsq: 0.8245 - val_loss: 0.0057 - val_mae: 0.0532 - val_mse: 0.0057 - val_rsq: 0.9280\n",
      "Epoch 38/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0138 - mae: 0.0863 - mse: 0.0138 - rsq: 0.8309 - val_loss: 0.0047 - val_mae: 0.0418 - val_mse: 0.0047 - val_rsq: 0.9413\n",
      "Epoch 39/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0131 - mae: 0.0856 - mse: 0.0131 - rsq: 0.8344 - val_loss: 0.0052 - val_mae: 0.0488 - val_mse: 0.0052 - val_rsq: 0.9344\n",
      "Epoch 40/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0123 - mae: 0.0835 - mse: 0.0123 - rsq: 0.8416 - val_loss: 0.0040 - val_mae: 0.0377 - val_mse: 0.0040 - val_rsq: 0.9494\n",
      "Epoch 41/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0123 - mae: 0.0828 - mse: 0.0123 - rsq: 0.8364 - val_loss: 0.0042 - val_mae: 0.0390 - val_mse: 0.0042 - val_rsq: 0.9469\n",
      "Epoch 42/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0120 - mae: 0.0822 - mse: 0.0120 - rsq: 0.8379 - val_loss: 0.0044 - val_mae: 0.0401 - val_mse: 0.0044 - val_rsq: 0.9446\n",
      "Epoch 43/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0126 - mae: 0.0813 - mse: 0.0126 - rsq: 0.8329 - val_loss: 0.0042 - val_mae: 0.0394 - val_mse: 0.0042 - val_rsq: 0.9477\n",
      "Epoch 44/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0127 - mae: 0.0849 - mse: 0.0127 - rsq: 0.8375 - val_loss: 0.0047 - val_mae: 0.0458 - val_mse: 0.0047 - val_rsq: 0.9415\n",
      "Epoch 45/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0123 - mae: 0.0829 - mse: 0.0123 - rsq: 0.8482 - val_loss: 0.0041 - val_mae: 0.0402 - val_mse: 0.0041 - val_rsq: 0.9484\n",
      "Epoch 46/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0127 - mae: 0.0831 - mse: 0.0127 - rsq: 0.8387 - val_loss: 0.0041 - val_mae: 0.0414 - val_mse: 0.0041 - val_rsq: 0.9480\n",
      "Epoch 47/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0121 - mae: 0.0817 - mse: 0.0121 - rsq: 0.8436 - val_loss: 0.0047 - val_mae: 0.0465 - val_mse: 0.0047 - val_rsq: 0.9405\n",
      "Epoch 48/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0117 - mae: 0.0819 - mse: 0.0117 - rsq: 0.8480 - val_loss: 0.0051 - val_mae: 0.0508 - val_mse: 0.0051 - val_rsq: 0.9359\n",
      "Epoch 49/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0127 - mae: 0.0821 - mse: 0.0127 - rsq: 0.8452 - val_loss: 0.0037 - val_mae: 0.0354 - val_mse: 0.0037 - val_rsq: 0.9539\n",
      "Epoch 50/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0139 - mae: 0.0821 - mse: 0.0139 - rsq: 0.8199 - val_loss: 0.0036 - val_mae: 0.0349 - val_mse: 0.0036 - val_rsq: 0.9547\n",
      "Epoch 51/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0113 - mae: 0.0807 - mse: 0.0113 - rsq: 0.8632 - val_loss: 0.0034 - val_mae: 0.0334 - val_mse: 0.0034 - val_rsq: 0.9566\n",
      "Epoch 52/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0116 - mae: 0.0798 - mse: 0.0116 - rsq: 0.8522 - val_loss: 0.0036 - val_mae: 0.0352 - val_mse: 0.0036 - val_rsq: 0.9543\n",
      "Epoch 53/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0115 - mae: 0.0808 - mse: 0.0115 - rsq: 0.8621 - val_loss: 0.0034 - val_mae: 0.0338 - val_mse: 0.0034 - val_rsq: 0.9567\n",
      "Epoch 54/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0144 - mae: 0.0808 - mse: 0.0144 - rsq: 0.8201 - val_loss: 0.0036 - val_mae: 0.0342 - val_mse: 0.0036 - val_rsq: 0.9553\n",
      "Epoch 55/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0121 - mae: 0.0789 - mse: 0.0121 - rsq: 0.8478 - val_loss: 0.0034 - val_mae: 0.0340 - val_mse: 0.0034 - val_rsq: 0.9566\n",
      "Epoch 56/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0116 - mae: 0.0790 - mse: 0.0116 - rsq: 0.8607 - val_loss: 0.0034 - val_mae: 0.0339 - val_mse: 0.0034 - val_rsq: 0.9570\n",
      "Epoch 57/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0113 - mae: 0.0799 - mse: 0.0113 - rsq: 0.8525 - val_loss: 0.0039 - val_mae: 0.0380 - val_mse: 0.0039 - val_rsq: 0.9514\n",
      "Epoch 58/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0117 - mae: 0.0798 - mse: 0.0117 - rsq: 0.8543 - val_loss: 0.0034 - val_mae: 0.0332 - val_mse: 0.0034 - val_rsq: 0.9575\n",
      "Epoch 59/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0125 - mae: 0.0825 - mse: 0.0125 - rsq: 0.8491 - val_loss: 0.0044 - val_mae: 0.0450 - val_mse: 0.0044 - val_rsq: 0.9451\n",
      "Epoch 60/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0104 - mae: 0.0764 - mse: 0.0104 - rsq: 0.8624 - val_loss: 0.0037 - val_mae: 0.0366 - val_mse: 0.0037 - val_rsq: 0.9534\n",
      "Epoch 61/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 37ms/step - loss: 0.0113 - mae: 0.0776 - mse: 0.0113 - rsq: 0.8535 - val_loss: 0.0034 - val_mae: 0.0329 - val_mse: 0.0034 - val_rsq: 0.9578\n",
      "Epoch 62/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0113 - mae: 0.0774 - mse: 0.0113 - rsq: 0.8519 - val_loss: 0.0052 - val_mae: 0.0522 - val_mse: 0.0052 - val_rsq: 0.9342\n",
      "Epoch 63/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0110 - mae: 0.0786 - mse: 0.0110 - rsq: 0.8620 - val_loss: 0.0033 - val_mae: 0.0335 - val_mse: 0.0033 - val_rsq: 0.9579\n",
      "Epoch 64/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0111 - mae: 0.0791 - mse: 0.0111 - rsq: 0.8581 - val_loss: 0.0034 - val_mae: 0.0338 - val_mse: 0.0034 - val_rsq: 0.9577\n",
      "Epoch 65/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0116 - mae: 0.0799 - mse: 0.0116 - rsq: 0.8651 - val_loss: 0.0032 - val_mae: 0.0318 - val_mse: 0.0032 - val_rsq: 0.9595\n",
      "Epoch 66/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0132 - mae: 0.0790 - mse: 0.0132 - rsq: 0.8365 - val_loss: 0.0046 - val_mae: 0.0461 - val_mse: 0.0046 - val_rsq: 0.9423\n",
      "Epoch 67/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0116 - mae: 0.0802 - mse: 0.0116 - rsq: 0.8559 - val_loss: 0.0034 - val_mae: 0.0332 - val_mse: 0.0034 - val_rsq: 0.9571\n",
      "Epoch 68/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0107 - mae: 0.0766 - mse: 0.0107 - rsq: 0.8577 - val_loss: 0.0033 - val_mae: 0.0323 - val_mse: 0.0033 - val_rsq: 0.9584\n",
      "Epoch 69/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0107 - mae: 0.0775 - mse: 0.0107 - rsq: 0.8635 - val_loss: 0.0035 - val_mae: 0.0358 - val_mse: 0.0035 - val_rsq: 0.9559\n",
      "Epoch 70/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0105 - mae: 0.0762 - mse: 0.0105 - rsq: 0.8693 - val_loss: 0.0039 - val_mae: 0.0406 - val_mse: 0.0039 - val_rsq: 0.9505\n",
      "Epoch 71/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0106 - mae: 0.0766 - mse: 0.0106 - rsq: 0.8634 - val_loss: 0.0031 - val_mae: 0.0316 - val_mse: 0.0031 - val_rsq: 0.9605\n",
      "Epoch 72/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0113 - mae: 0.0782 - mse: 0.0113 - rsq: 0.8529 - val_loss: 0.0032 - val_mae: 0.0320 - val_mse: 0.0032 - val_rsq: 0.9597\n",
      "Epoch 73/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0109 - mae: 0.0779 - mse: 0.0109 - rsq: 0.8620 - val_loss: 0.0031 - val_mae: 0.0311 - val_mse: 0.0031 - val_rsq: 0.9610\n",
      "Epoch 74/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0111 - mae: 0.0769 - mse: 0.0111 - rsq: 0.8636 - val_loss: 0.0031 - val_mae: 0.0310 - val_mse: 0.0031 - val_rsq: 0.9607\n",
      "Epoch 75/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0102 - mae: 0.0758 - mse: 0.0102 - rsq: 0.8729 - val_loss: 0.0032 - val_mae: 0.0322 - val_mse: 0.0032 - val_rsq: 0.9595\n",
      "Epoch 76/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 36ms/step - loss: 0.0107 - mae: 0.0772 - mse: 0.0107 - rsq: 0.8609 - val_loss: 0.0031 - val_mae: 0.0311 - val_mse: 0.0031 - val_rsq: 0.9613\n",
      "Epoch 77/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0110 - mae: 0.0774 - mse: 0.0110 - rsq: 0.8658 - val_loss: 0.0031 - val_mae: 0.0316 - val_mse: 0.0031 - val_rsq: 0.9608\n",
      "Epoch 78/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0105 - mae: 0.0768 - mse: 0.0105 - rsq: 0.8665 - val_loss: 0.0033 - val_mae: 0.0339 - val_mse: 0.0033 - val_rsq: 0.9589\n",
      "Epoch 79/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0104 - mae: 0.0767 - mse: 0.0104 - rsq: 0.8677 - val_loss: 0.0030 - val_mae: 0.0307 - val_mse: 0.0030 - val_rsq: 0.9617\n",
      "Epoch 80/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0112 - mae: 0.0775 - mse: 0.0112 - rsq: 0.8662 - val_loss: 0.0031 - val_mae: 0.0307 - val_mse: 0.0031 - val_rsq: 0.9608\n",
      "Epoch 81/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0105 - mae: 0.0752 - mse: 0.0105 - rsq: 0.8626 - val_loss: 0.0031 - val_mae: 0.0310 - val_mse: 0.0031 - val_rsq: 0.9605\n",
      "Epoch 82/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0111 - mae: 0.0774 - mse: 0.0111 - rsq: 0.8591 - val_loss: 0.0031 - val_mae: 0.0311 - val_mse: 0.0031 - val_rsq: 0.9606\n",
      "Epoch 83/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0107 - mae: 0.0766 - mse: 0.0107 - rsq: 0.8632 - val_loss: 0.0034 - val_mae: 0.0348 - val_mse: 0.0034 - val_rsq: 0.9570\n",
      "Epoch 84/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0098 - mae: 0.0748 - mse: 0.0098 - rsq: 0.8718 - val_loss: 0.0030 - val_mae: 0.0304 - val_mse: 0.0030 - val_rsq: 0.9619\n",
      "Epoch 85/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0107 - mae: 0.0774 - mse: 0.0107 - rsq: 0.8723 - val_loss: 0.0030 - val_mae: 0.0306 - val_mse: 0.0030 - val_rsq: 0.9618\n",
      "Epoch 86/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0103 - mae: 0.0771 - mse: 0.0103 - rsq: 0.8624 - val_loss: 0.0030 - val_mae: 0.0305 - val_mse: 0.0030 - val_rsq: 0.9621\n",
      "Epoch 87/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0107 - mae: 0.0774 - mse: 0.0107 - rsq: 0.8680 - val_loss: 0.0030 - val_mae: 0.0305 - val_mse: 0.0030 - val_rsq: 0.9620\n",
      "Epoch 88/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0118 - mae: 0.0769 - mse: 0.0118 - rsq: 0.8507 - val_loss: 0.0031 - val_mae: 0.0307 - val_mse: 0.0031 - val_rsq: 0.9615\n",
      "Epoch 89/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0108 - mae: 0.0756 - mse: 0.0108 - rsq: 0.8602 - val_loss: 0.0031 - val_mae: 0.0315 - val_mse: 0.0031 - val_rsq: 0.9607\n",
      "Epoch 90/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0099 - mae: 0.0748 - mse: 0.0099 - rsq: 0.8767 - val_loss: 0.0030 - val_mae: 0.0304 - val_mse: 0.0030 - val_rsq: 0.9620\n",
      "Epoch 91/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0108 - mae: 0.0751 - mse: 0.0108 - rsq: 0.8638 - val_loss: 0.0031 - val_mae: 0.0312 - val_mse: 0.0031 - val_rsq: 0.9614\n",
      "Epoch 92/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0101 - mae: 0.0752 - mse: 0.0101 - rsq: 0.8703 - val_loss: 0.0031 - val_mae: 0.0307 - val_mse: 0.0031 - val_rsq: 0.9615\n",
      "Epoch 93/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0099 - mae: 0.0752 - mse: 0.0099 - rsq: 0.8730 - val_loss: 0.0031 - val_mae: 0.0309 - val_mse: 0.0031 - val_rsq: 0.9614\n",
      "Epoch 94/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0105 - mae: 0.0759 - mse: 0.0105 - rsq: 0.8643 - val_loss: 0.0031 - val_mae: 0.0317 - val_mse: 0.0031 - val_rsq: 0.9605\n",
      "Epoch 95/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0107 - mae: 0.0755 - mse: 0.0107 - rsq: 0.8700 - val_loss: 0.0030 - val_mae: 0.0306 - val_mse: 0.0030 - val_rsq: 0.9618\n",
      "Epoch 96/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0110 - mae: 0.0761 - mse: 0.0110 - rsq: 0.8575 - val_loss: 0.0030 - val_mae: 0.0306 - val_mse: 0.0030 - val_rsq: 0.9617\n",
      "Epoch 97/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0100 - mae: 0.0753 - mse: 0.0100 - rsq: 0.8757 - val_loss: 0.0033 - val_mae: 0.0330 - val_mse: 0.0033 - val_rsq: 0.9590\n",
      "Epoch 98/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0113 - mae: 0.0770 - mse: 0.0113 - rsq: 0.8597 - val_loss: 0.0032 - val_mae: 0.0326 - val_mse: 0.0032 - val_rsq: 0.9595\n",
      "Epoch 99/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 0.0112 - mae: 0.0760 - mse: 0.0112 - rsq: 0.8565 - val_loss: 0.0031 - val_mae: 0.0310 - val_mse: 0.0031 - val_rsq: 0.9612\n",
      "Epoch 100/100\n",
      "\u001b[1m50/50\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 34ms/step - loss: 0.0122 - mae: 0.0779 - mse: 0.0122 - rsq: 0.8481 - val_loss: 0.0030 - val_mae: 0.0305 - val_mse: 0.0030 - val_rsq: 0.9618\n",
      "\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0030 - mae: 0.0307 - mse: 0.0030 - rsq: 0.9639\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>VAL SET<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">LOSS</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0030</span>, <span style=\"color: #808000; text-decoration-color: #808000\">MAE</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0305</span>, <span style=\"color: #808000; text-decoration-color: #808000\">MSE</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0030</span>, <span style=\"color: #808000; text-decoration-color: #808000\">RSQ</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.9621</span>                                <a href=\"file:///tmp/ipykernel_626139/3247659150.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3247659150.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///tmp/ipykernel_626139/3247659150.py#114\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">114</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mVAL SET\u001b[1m]\u001b[0m \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0030\u001b[0m, \u001b[33mMAE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0305\u001b[0m, \u001b[33mMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.0030\u001b[0m, \u001b[33mRSQ\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.9621\u001b[0m                                \u001b]8;id=187434;file:///tmp/ipykernel_626139/3247659150.py\u001b\\\u001b[2m3247659150.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=786914;file:///tmp/ipykernel_626139/3247659150.py#114\u001b\\\u001b[2m114\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "task.train(params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finally we will evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m39/39\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0030 - mae: 0.0307 - mse: 0.0030 - rsq: 0.9639\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> <span style=\"font-weight: bold\">[</span>TEST SET<span style=\"font-weight: bold\">]</span> <span style=\"color: #808000; text-decoration-color: #808000\">LOSS</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.30</span>%, <span style=\"color: #808000; text-decoration-color: #808000\">MAE</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3.05</span>%, <span style=\"color: #808000; text-decoration-color: #808000\">MSE</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.30</span>%, <span style=\"color: #808000; text-decoration-color: #808000\">RSQ</span>=<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">96.21</span>%                                   <a href=\"file:///tmp/ipykernel_626139/3848634147.py\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">3848634147.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file:///tmp/ipykernel_626139/3848634147.py#29\" target=\"_blank\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">29</span></a>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[34mINFO    \u001b[0m \u001b[1m[\u001b[0mTEST SET\u001b[1m]\u001b[0m \u001b[33mLOSS\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.30\u001b[0m%, \u001b[33mMAE\u001b[0m=\u001b[1;36m3\u001b[0m\u001b[1;36m.05\u001b[0m%, \u001b[33mMSE\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.30\u001b[0m%, \u001b[33mRSQ\u001b[0m=\u001b[1;36m96\u001b[0m\u001b[1;36m.21\u001b[0m%                                   \u001b]8;id=872574;file:///tmp/ipykernel_626139/3848634147.py\u001b\\\u001b[2m3848634147.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=819072;file:///tmp/ipykernel_626139/3848634147.py#29\u001b\\\u001b[2m29\u001b[0m\u001b]8;;\u001b\\\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m312/312\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 900x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "task.evaluate(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
